Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10953/2121
Título: Sorting Olive Batches for the Milling Process Using Image Processing
Autoría: Aguilera Puerto, Daniel
Martínez Gila, Diego Manuel
Gámez García, Javier
Gómez Ortega, Juan
Resumen: The quality of virgin olive oil obtained in the milling process is directly bound to the characteristics of the olives. Hence, the correct classification of the different incoming olive batches is crucial to reach the maximum quality of the oil. The aim of this work is to provide an automatic inspection system, based on computer vision, and to classify automatically different batches of olives entering the milling process. The classification is based on the differentiation between ground and tree olives. For this purpose, three different species have been studied (Picudo, Picual and Hojiblanco). The samples have been obtained by picking the olives directly from the tree or from the ground. The feature vector of the samples has been obtained on the basis of the olive image histograms. Moreover, different image preprocessing has been employed, and two classification techniques have been used: these are discriminant analysis and neural networks. The proposed methodology has been validated successfully, obtaining good classification results.
Palabras clave: olive classification
computer vision
automatic quality control
Fecha: 2-jul-2015
Patrocinador: DPI2011-27284, TEP2009-5363 and AGR-6616
Editorial: MDPI
Citación: Puerto, D., Gila, D., García, J., & Ortega, J. (2015). Sorting Olive Batches for the Milling Process Using Image Processing. Sensors, 15(7), 15738–15754. https://doi.org/10.3390/s150715738
Aparece en las colecciones: DIEA-Artículos

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